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feat: strategy builder

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OpenSquared
2026-07-27 18:58:02 +02:00
parent ce09159bfb
commit 568414ca0c
18 changed files with 1315 additions and 63 deletions
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44
backend/services/options_pricer.py
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@@ -6,17 +6,30 @@ import math
def black_scholes(S: float, K: float, T: float, r: float, sigma: float, option_type: str = "call") -> Dict[str, float]:
"""Black-Scholes pricing + Greeks."""
"""Black-Scholes pricing + Greeks (first-order delta/gamma/theta/vega/rho, plus the
second-order Greeks used by Strategy Builder's "advanced sensitivities" panel: vanna,
charm, vomma/volga, veta, speed, color, zomma — vera deliberately omitted, see project
memory "Strategy Builder Greeks plan"). All second-order values are scaled to match the
convention their related first-order Greek already uses here — e.g. vanna/vomma/zomma
are "per vol POINT" like vega already is (not per unit of raw decimal sigma), charm/
color/veta are "per DAY" like theta already is (not per year) — every formula/scaling
is verified against finite-difference bumps of this same function's own first-order
outputs (see scratchpad test_second_order_greeks.py from the Phase 3 build), not just
hand-derived from a textbook, since these third-derivative formulas are easy to get
subtly wrong."""
S = float(S or 100.0)
K = float(K or S)
T = float(T or 0.001)
sigma = float(sigma or 0.25)
if T <= 0 or sigma <= 0:
intrinsic = max(0, S - K) if option_type == "call" else max(0, K - S)
return {"price": intrinsic, "delta": 0, "gamma": 0, "theta": 0, "vega": 0, "rho": 0}
return {"price": intrinsic, "delta": 0, "gamma": 0, "theta": 0, "vega": 0, "rho": 0,
"vanna": 0, "charm": 0, "vomma": 0, "veta": 0, "speed": 0, "color": 0, "zomma": 0}
d1 = (math.log(S / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * math.sqrt(T))
d2 = d1 - sigma * math.sqrt(T)
sqrtT = math.sqrt(T)
d1 = (math.log(S / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * sqrtT)
d2 = d1 - sigma * sqrtT
phi_d1 = norm.pdf(d1)
if option_type == "call":
price = S * norm.cdf(d1) - K * math.exp(-r * T) * norm.cdf(d2)
@@ -27,9 +40,19 @@ def black_scholes(S: float, K: float, T: float, r: float, sigma: float, option_t
delta = norm.cdf(d1) - 1
rho = -K * T * math.exp(-r * T) * norm.cdf(-d2) / 100
gamma = norm.pdf(d1) / (S * sigma * math.sqrt(T))
theta = (-(S * norm.pdf(d1) * sigma) / (2 * math.sqrt(T)) - r * K * math.exp(-r * T) * norm.cdf(d2 if option_type == "call" else -d2)) / 365
vega = S * norm.pdf(d1) * math.sqrt(T) / 100
gamma = phi_d1 / (S * sigma * sqrtT)
theta = (-(S * phi_d1 * sigma) / (2 * sqrtT) - r * K * math.exp(-r * T) * norm.cdf(d2 if option_type == "call" else -d2)) / 365
vega = S * phi_d1 * sqrtT / 100
# Second-order — same for calls and puts (this pricer carries no dividend yield, so the
# extra q-term that would otherwise make charm/veta/color differ by option_type is zero).
vanna = (-phi_d1 * d2 / sigma) / 100
vomma = (S * phi_d1 * sqrtT * d1 * d2 / sigma) / 10_000
charm = (-phi_d1 * (2 * r * T - d2 * sigma * sqrtT) / (2 * T * sigma * sqrtT)) / 365
veta = (S * phi_d1 * sqrtT * ((r * d1) / (sigma * sqrtT) - (1 + d1 * d2) / (2 * T))) / 36_500
speed = -(gamma / S) * (d1 / (sigma * sqrtT) + 1)
color = (phi_d1 / (2 * S * T * sigma * sqrtT) * (2 * r * T + 1 + d1 * (2 * r * T - d2 * sigma * sqrtT) / (sigma * sqrtT))) / 365
zomma = (gamma * (d1 * d2 - 1) / sigma) / 100
return {
"price": round(price, 4),
@@ -38,6 +61,13 @@ def black_scholes(S: float, K: float, T: float, r: float, sigma: float, option_t
"theta": round(theta, 4),
"vega": round(vega, 4),
"rho": round(rho, 4),
"vanna": round(vanna, 6),
"charm": round(charm, 6),
"vomma": round(vomma, 6),
"veta": round(veta, 6),
"speed": round(speed, 8),
"color": round(color, 8),
"zomma": round(zomma, 6),
}